Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network

Medical diagnosis can be severely hindered by distorted medical images, especially in the analysis of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images. Therefore, enhancing the accuracy of diagnostic imaging and inpainting damaged areas are essential for medical diagnosis. Over t...

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Veröffentlicht in:	Sadhana (Bangalore) Jg. 49; H. 2; S. 181
Hauptverfasser:	Kumar, Puranam Revanth, Shilpa, B, Jha, Rajesh Kumar, Raju, B Deevena, Mohammed, Thayyaba Khatoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New Delhi Springer India 18.05.2024 Springer Nature B.V
Schlagworte:	Algorithms Computed tomography Computer vision Deep learning Design Diagnosis Engineering Human error Image quality Image reconstruction Magnetic resonance imaging Medical imaging Neural networks Optimization techniques Parameter robustness Pixels Signal to noise ratio Tomography Deep learning Medical image inpainting Object removal Image translation Medical image analysis
ISSN:	0973-7677, 0256-2499, 0973-7677
Online-Zugang:	Volltext
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